Real-time validation of distributed energy resource device commitments

ABSTRACT

A distributed energy resource (DER) device is coupled to a utility meter in a “behind-the-meter” configuration. The utility meter analyzes a commitment generated by the DER device to determine a specific operation performed by the DER device at a particular time. The utility meter analyzes metrology data to identify an “event” associated with the particular time and then attempts to map the identified event back to the DER device based on a library of events associated with different DER devices. The utility meter also attempts to map the identified event to the specific operation set forth in the commitment. If the utility meter can successfully map the identified event to both the DER device and to the specific operation set forth in the commitment, then the utility meter generates a validated commitment. The validated commitment can be used to facilitate an energy market settlement process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority benefit of U.S. provisional patentapplication titled, “Real-Time Validation of Distributed Energy ResourceDevice Commitments,” filed on Jan. 31, 2019 and having Ser. No.62/799,673. The subject matter of this related application is herebyincorporated herein by reference.

BACKGROUND Field of the Various Embodiments

Embodiments of the present invention relate generally to utilitynetworks and utility distribution infrastructures and, morespecifically, to real-time validation of distributed energy resourcedevice commitments.

Description of the Related Art

In conventional utility networks and utility distributioninfrastructures, one or more utility providers generate resources thatare distributed to one or more downstream resource consumers. Forexample, a nuclear power plant could generate electricity that is thendistributed to various electricity consumers within a city. A givendownstream resource consumer usually has a utility meter that monitorsvarious characteristics associated with the distribution and/orconsumption of resources. For example, a residence could have anelectricity meter that monitors the rate at which the residence consumeselectricity, the total amount of electricity distributed to theresidence over a particular period of time, and so forth.

In more modern utility networks and utility distributioninfrastructures, the downstream resource consumers sometimes havedistributed energy resource (DER) devices that are configured to helpdistribute, generate, and/or store resources. One example of a DERdevice is a smart thermostat that, under specific circumstances, limitsthe amount of electricity being consumed by a consumer. Another exampleof a DER device is a solar power system that generates solar-basedelectricity for consumption by one or more consumers and/or fordistribution onto a utility distribution infrastructure. Yet anotherexample of a DER device is a home battery system that stores electricitythat can be subsequently consumed during periods of peak electricitydemand. As a general matter, DER devices are deployed to reduce costsand to improve the efficiency with which resources can be distributed toconsumers.

In certain situations, a consumer can configure a given DER device toparticipate in one or more energy markets in order to earn financialincentives. For example, a consumer could configure a smart thermostatto participate in a demand-response program offered by a utilityprovider in exchange for a rebate or other form of compensation. As partof such a program, the consumer could configure the smart thermostat toturn off climate control at the residence of the consumer whenever theutility provider instructs the smart thermostat to do so, and theconsumer would be compensated for participating in the program. Amongother things, these types of arrangements enable utility providers tolimit how much electricity is consumed during periods of peakelectricity demand.

When a DER device participates in an energy market, the DER device isusually configured to operate in accordance with the terms of acontract. For example, a smart thermostat could be configured to operatein accordance with a contract that specifies the particularcircumstances under which a utility provider can cause the smartthermostat to turn off or turn off climate control. The owners of suchcontracts can be utility providers, but, more typically, the contractowners are third-party vendors that manage the participation of DERdevices in various energy markets on behalf of the utility providers.

Contracts that manage the participation of DER devices in energy marketscan specify a wide variety of different operations that participatingDER devices need to perform. One requirement found in some contracts isthat a participating DER device should generate “commitments” thatindicate the specific actions the DER device implemented to fulfillvarious terms of the relevant contract. For example, a smart thermostatcould generate a commitment indicating that the smart thermostat turnedoff climate control at the residence of a given consumer when instructedto do so by the relevant utility provider. Upon receiving a commitment,the contract owner analyzes the commitment to confirm that the terms ofthe contract have been met and thereby validate the commitment. Thecontract owner then compensates the consumer for participation in theenergy market. The process of validating commitments and compensatingconsumers is referred to as the “settlement” process.

One drawback of the above approach is that the settlement process cantake three months or more to complete. Consequently, consumers have towait for extended periods of time to receive compensation whenparticipating in energy market programs. These lengthy wait times candissuade consumers from participating in energy market programs. Iffewer consumers participate in energy market programs, then utilityproviders may have greater difficulty providing resources to consumersefficiently and at reduced cost.

As the foregoing illustrates, what is needed in the art are moreeffective ways to validate DER device commitments.

SUMMARY

Some embodiments include a computer-implemented method for validatingdistributed resource device commitments, including determining that afirst commitment received from a first distributed resource deviceindicates that the first distributed resource device modified how muchof a resource was consumed at a first location during a first timeinterval, generating first metrology data that indicates how much of theresource was consumed at the first location during the first timeinterval, and validating the first commitment based on the firstmetrology data associated with the first location.

One technological advantage of the disclosed approach relative to theprior art is that commitments reported by distributed resource devicesare validated automatically by the utility meter, which enables thesettlement process to be performed much faster relative to conventionalapproaches.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the variousembodiments can be understood in detail, a more particular descriptionof the inventive concepts, briefly summarized above, may be had byreference to various embodiments, some of which are illustrated in theappended drawings. It is to be noted, however, that the appendeddrawings illustrate only typical embodiments of the inventive conceptsand are therefore not to be considered limiting of scope in any way, andthat there are other equally effective embodiments.

FIG. 1 illustrates a network system configured to implement one or moreaspects of the present embodiments;

FIG. 2 illustrates an exemplary behind-the-meter configuration,according to various embodiments:

FIG. 3 is a more detailed illustration of the node of FIG. 2 , accordingto various embodiments:

FIG. 4 is a more detailed illustration of the validation application ofFIG. 3 , according to various embodiments;

FIG. 5 is a graph of metrology data that is processed by the node ofFIG. 2 when validating a commitment, according to various embodiments;and

FIG. 6 is a flow diagram of method steps for validating DER devicecommitments, according to various embodiments.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth toprovide a more thorough understanding of the various embodiments.However, it will be apparent to one of skilled in the art that theinventive concepts may be practiced without one or more of thesespecific details.

As noted above, DER devices can be configured to participate in energymarkets in order to earn financial incentives for a consumer. Toparticipate in a given energy market, the consumer or a third partyconfigures the given DER device to operate in accordance with a contractthat at least partially defines the operation of the DER device. Whenthe DER device performs various operations in accordance with thecontract, the DER device also generates a “commitment” indicating thespecific actions the DER device performs to fulfil the terms of thecontract. A contract holder, such as a utility provider or a third-partyintermediary, then validates the commitment and compensates the consumervia a process that is referred to as “settlement.”

One drawback associated with a conventional settlement process is thatvalidating commitments generally takes a long time, sometimes in excessof three months. For example, suppose a given DER device participates inan energy market and reports commitments to a contract holder toinitiate the settlement process. To validate a given commitment, ananalyst associated with the contract holder would first have to analyzethe commitment to identify the particular operations performed by theDER device. The analyst would then have to analyze a contract associatedwith the energy market to determine whether those operations meet theterms of the contract. The analyst would also have to access utilitymeter data associated with the location where the DER device resides inorder to verify that the DER device did, indeed, perform the reportedoperations. Specifically, the analyst would have to analyze the meterdata during a time interval where the reported operations are supposedto have occurred, and then determine whether the meter data supports theoccurrence of these operations. Depending on the availability of data,these analyses are carried out at varying levels of aggregation andaccuracy.

The steps described above are manually performed by the analyst and cantherefore take a long time. Further, contract holders are oftentimesbacklogged with commitments and therefore cannot begin the settlementprocess immediately. These various delays ultimately cause the consumerto have to wait to be compensated for participating in the energymarket. Consumers usually do not appreciate having to wait forcompensation. As such, when the settlement process takes a long time,consumers may find participating in energy markets less desirable.Energy markets help improve the efficiency with which resources can bedistributed. When fewer consumers participate in energy markets, utilityproviders can have difficulty providing consumers with resourcesefficiently and/or at low cost.

To address these issues, embodiments of the invention include a smartutility meter that validates commitments generated by DER devicescoupled behind the smart utility meter. The DER devices reside in alocation where the utility meter monitors the distribution of resources.The DER devices can modify the distribution, generation, and/or storageof resources at the location in accordance with, at least to somedegree, a contract associated with an energy market. When a given DERdevice operates in accordance with a given contract, the DER devicereports a commitment to the smart utility meter indicating the specificoperations the DER device performs to fulfill the terms of the contract.

The utility meter analyzes the commitment to determine a time intervalwhen the reported operations were to occur. The utility meter thenanalyzes meter data associated with the distribution of resources at thelocation to identify any events associated with the time interval. Uponidentifying an event, the utility meter can map the event to the DERdevice based on a library of events corresponding to different DERdevices. The utility meter can also map the event to the reportedoperation. If the utility meter successfully maps the event to both theDER device and the reported operations, then the occurrence of theoperations is confirmed and the DER device generates a validatedcommitment. The validated commitment can be transmitted to relevantparties in order to expedite the settlement process.

One technological advantage of the disclosed approach relative to theprior art is that commitments reported by DER devices are validatedautomatically by the utility meter, which enables the settlement processto be performed much faster relative to conventional approaches.Accordingly, consumers can be compensated for participating in energymarkets much faster than the three-month or longer periods typicallyassociated with conventional settlement processes. This technologicaladvantage represents at least one technological advancement relative toprior art approaches.

System Overview

FIG. 1 illustrates a network system configured to implement one or moreaspects of the present embodiments. As shown, network system 100includes a field area network (FAN) 110, a wide area network (WAN)backhaul 120, and a control center 130. FAN 110 is coupled to controlcenter 130 via WAN backhaul 120. Control center 130 is configured tocoordinate the operation of FAN 110.

FAN 110 includes personal area network (PANS) A, B, and C. PANs A and Bare organized according to a mesh network topology, while PAN C isorganized according to a star network topology. Each of PANs A, B, and Cincludes at least one border router node 112 and one or more mainspowered device (MPD) nodes 114. PANs B and C further include one or morebattery powered device (BPD) nodes 116.

MPD nodes 114 draw power from an external power source, such as mainselectricity or a power grid. MPD nodes 114 typically operate on acontinuous basis without powering down for extended periods of time. BPDnodes 116 draw power from an internal power source, such as a battery.BPD nodes 116 typically operate intermittently and power down forextended periods of time in order to conserve battery power. MPD nodes114 and BPD nodes 116 are configured to gather sensor data, process thesensor data, and communicate data processing results and otherinformation to control center 130. Border router nodes 112 operate asaccess points to provide MPD nodes 114 and BPD nodes 116 with access tocontrol center 130.

Any of border router nodes 112, MPD nodes 114, and BPD nodes 116 areconfigured to communicate directly with one or more adjacent nodes viabi-directional communication links. The communication links may be wiredor wireless links, although in practice, adjacent nodes of a given PANexchange data with one another by transmitting data packets via wirelessradio frequency (RF) communications. The various node types areconfigured to perform a technique known in the art as “channel hopping”in order to periodically receive data packets on varying channels. Asknown in the art, a “channel” may correspond to a particular range offrequencies. In one embodiment, a node may compute a current receivechannel by evaluating a Jenkins hash function based on a total number ofchannels, the media access control (MAC) address of the node, and otherinformation associated with the node.

Each node within a given PAN may implement a discovery protocol toidentify one or more adjacent nodes or “neighbors.” A node that hasidentified an adjacent, neighboring node may establish a bi-directionalcommunication link with the neighboring node. Each neighboring node mayupdate a respective neighbor table to include information concerning theother node, including the MAC address of the other node as well as areceived signal strength indication (RSSI) of the communication linkestablished with that node.

Nodes may compute the channel hopping sequences of adjacent nodes tofacilitate the successful transmission of data packets to those nodes.In embodiments where nodes implement the Jenkins hash function, a nodecomputes a current receive channel of an adjacent node using the totalnumber of channels, the MAC address of the adjacent node, and a timeslot number assigned to a current time slot of the adjacent node.

Any of the nodes discussed above may operate as a source node, anintermediate node, or a destination node for the transmission of datapackets. A given source node may generate a data packet and thentransmit the data packet to a destination node via any number ofintermediate nodes (in mesh network topologies). The data packet mayindicate a destination for the packet and/or a particular sequence ofintermediate nodes to traverse in order to reach the destination node.In one embodiment, each intermediate node may include a forwardingdatabase indicating various network routes and cost metrics associatedwith each route.

Nodes may transmit data packets across a given PAN and across WANbackhaul 120 to control center 130. Similarly, control center 130 maytransmit data packets across WAN backhaul 120 and across any given PANto a particular node included therein. As a general matter, numerousroutes may exist which traverse any of PANs A, B, and C and include anynumber of intermediate nodes, thereby allowing any given node or othercomponent within network system 100 to communicate with any other nodeor component included therein.

Control center 120 includes one or more server machines (not shown)configured to operate as sources for, or destinations of, data packetsthat traverse within network system 100. The server machines may querynodes within network system 100 to obtain various data, including raw orprocessed sensor data, power consumption data, node/network throughputdata, status information, and so forth. The server machines may alsotransmit commands and/or program instructions to any node within networksystem 100 to cause those nodes to perform various operations. In oneembodiment, each server machine is a computing device configured toexecute, via a processor, a software application stored in a memory toperform various network management operations.

Nodes may likewise include computing device hardware configured toperform processing operations and execute program code. Each node mayfurther include various analog-to-digital and digital-to-analogconverters, digital signal processors (DSPs), harmonic oscillators,transceivers, and any other components generally associated withRF-based communication hardware.

Any of the nodes discussed above can be configured as a smart utilitymeter and be coupled to a utility line in order to monitor thedistribution of resources at a particular location via the utility line.For example, an MPD node 114 could be configured as a smart electricmeter and be coupled to a power line in order to monitor thedistribution of electricity at a residence via the power line. A givennode that is configured as a smart utility meter generates real-timemetrology data quantifying the distribution of resources at the locationwhere the given node resides. The given node then periodically reportsthis metrology data to control center 130 via FAN 110.

A given node that is configured as a smart utility meter can be coupledto, and configured to interoperate with, one or more DER devices in a“behind-the-meter” configuration. As referred to herein, abehind-the-meter or BTM configuration generally includes a node that iscoupled to a utility line and one or more DER devices that are coupledto the utility line downstream of the node. The node monitors thedistribution of resources via the utility line. The DER devices modifythe distribution of resources via the utility line in a detectablemanner. An exemplary behind-the-meter configuration is described ingreater detail below in conjunction with FIG. 2 .

Exemplary Behind-the-Meter Configuration

FIG. 2 illustrates an exemplary behind-the-meter (BTM) configuration,according to various embodiments. As shown, BTM configuration 200includes DER devices 210(0) through 210(N), a node 220, and utility line230. DER devices 210 are coupled to node 220. DER devices 210 and node220 are coupled to a utility line 230. In one embodiment, DER devices210 may be coupled indirectly to utility line 230 via node 220. Inanother embodiment, DER devices 210 may be connected directly to utilityline 230.

Utility line 230 is configured to distribute resources to a givenlocation where BTM configuration 200 is deployed. For example, utilityline 230 could be a power line that distributes electricity to aresidence. Node 220 is configured as a smart utility meter that monitorsthe distribution of resources via utility line 230 to generate metrologydata 222. For example, node 220 could be a smart electric meterconfigured to generate metrology data that reflects the distribution ofelectricity via utility line 230. Node 220 may be any of the node typesdiscussed above in conjunction with FIG. 1 . Metrology data 222quantifies the distribution and/or consumption of resources at thelocation where node 220 is deployed. Metrology data 222 may have anytechnically feasible resolution, although in practice metrology data 222has sufficient resolution to identify relevant DER-oriented events(e.g., one-second resolution).

Each DER device 210 is configured to perform various operations tomodify the distribution of resources via utility line 230. For example,DER device 210(0) could be a smart thermostat that limits theconsumption of electricity via utility line 230 by modifying climatecontrol settings. Alternatively, DER device 210(0) could be a solarpower system configured to generate electricity and distribute at leasta portion of that electricity onto utility line 230. DER device 210(0)could also be a home battery system configured to draw electricity fromutility line 230 for storage.

A given DER device 210 can modify the distribution of resources based onthe terms of a contract associated with an energy market. In so doing,the given DER device 210 may generate one or more commitments 212 thatindicate the specific operation(s) the DER device 210 performs over oneor more time intervals to fulfill the terms of the correspondingcontract. As is shown, DER devices 210(0) through 210(N) generatecommitments 212(0) through 212(N) and then transmit these commitments tonode 220.

Node 220 is configured to perform a real-time validation of commitments212 based on metrology data 222. Node 220 validates commitments 212 toconfirm that DER devices 210 operate in accordance with any associatedenergy market contracts. As is shown, node 220 generates validatedcommitments 224 based on commitments 212(0) through 212(N) and metrologydata 222. Node 220 performs various operations to validate a givencommitment 224, as described in greater detail below in conjunction withFIGS. 3-6 .

Node 220 transmits validated commitments 224 to control center 130 tofacilitate various settlement processes associated with the energymarket(s) in which DER devices 210 participate. Because node 220 hasalready confirmed that commitments 212 accurately reflect the variousoperations DER devices 210 perform to participate in the energymarket(s), those settlement processes can be performed expeditiously andwith minimal oversight. Accordingly, the real-time validation performedby node 220 can accelerate how quickly consumers are compensated forparticipation in energy markets. Node 220 is described in greater detailbelow in conjunction with FIG. 3 .

Node Hardware Components

FIG. 3 is a more detailed illustration of the node of FIG. 2 , accordingto various embodiments. As shown, node 220 includes a computing device300 coupled to a transceiver 340 and an oscillator 350. Computing device300 coordinates the operations of node 220. Transceiver 340 isconfigured to transmit and receive data packets across network system100 using a range of channels and power levels. Oscillator 350 providesone or more oscillation signals according to which the transmission andreception of data packets can be scheduled. Node 220 may be used toimplement any of border router nodes 112, MPD nodes 114, and BPD nodes116 of FIG. 1 .

Computing device 300 includes a processor 310, input/output (I/O) 320,and memory 330, coupled together. Processor 310 may include any hardwareconfigured to process data and execute software applications. Processor310 may include a real-time clock (RTC) (not shown) according to whichprocessor 310 maintains an estimate of the current time. I/O devices 320include devices configured to receive input, devices configured toprovide output, and devices configured to both receive input and provideoutput. Memory 330 may be implemented by any technically feasiblestorage medium.

Memory 330 includes a validation application 332. Validation application332 includes program code that, when executed by processor 310, analyzesone or more commitments 212 based on metrology data 222 to generatevalidated commitments 224. To validate a given commitment 212 associatedwith a given DER device 210, node 220 analyzes the commitment 212 todetermine a specific operation the DER device 210 performs during aparticular time interval. Node 220 then analyzes metrology data 222 toidentify an “event” associated with that particular time interval. Theevent could be, for example, a change in resource distribution thatexceeds a threshold during the time interval, among other possibleevents. Node 220 attempts to map the identified event back to the DERdevice 210 based on a library of events associated with different DERdevices. In one embodiment, the library of events may include varioussignature patterns of activity that can be uniquely associated withspecific DER devices. Node 220 also attempts to map the identified eventto the specific operation set forth in the commitment 212. If node 220successfully maps the identified event to the DER device 210 and to thespecific operation set forth in commitment 212, then node 220 generatesa validated commitment 224 confirming that the DER device 210 isresponsible for performing the specific operation. Validationapplication 332 transmits the validated commitment 224 to control center230 to facilitate a settlement process associated with an energy marketin which the DER device 210 participates. Validation application 332 isdescribed in greater detail below in conjunction with FIG. 4 .

Node Software Modules

FIG. 4 is a more detailed illustration of the validation application ofFIG. 3 , according to various embodiments. As shown, validationapplication 332 includes a distribution monitor 400, an event analyzer410, and a commitment validator 420. These various software modulesinteroperate to generate a validated commitment 224 based on metrologydata 222 and a commitment 212, as described in greater detail herein.

Distribution monitor 400 is coupled to utility line 230 and configuredto measure the real-time distribution of resources via utility line 230to generate metrology data 222. Metrology data 222 includes one or moredifferent channels of data collected over any technically feasible timeperiod and with any resolution. As previously mentioned, in practice,metrology data 222 has one-second resolution.

Event analyzer 410 analyzes metrology data 222 to generate adistribution event 402. Distribution event 402 indicates some type offluctuation in the distribution of resources. For example, distributionevent 402 could indicate that the distribution of resources fell beneatha threshold level during a particular time interval. Event analyzer 410can be configured to detect any technically feasible type ofdistribution event based on metrology data 222. Distribution events arediscussed in greater detail below in conjunction with FIG. 5 .

Event analyzer 410 queries event models 412 based on distribution event402 to identify a particular event model 412 that corresponds todistribution event 402. A given event model 412 generally describes atime-varying pattern of resource distribution fluctuations that can beattributed to the operation of a DER device 210. In one embodiment, eachevent model 412 may indicate an electrical signature associated with aspecific type of DER device 210 when performing a specific operation,such as powering on, powering off, or changing state. For example, oneevent model 412 could specify a decrease in electricity consumption thathas specific characteristics indicative of a smart thermostatdeactivating an air conditioning unit. Event models 412 can includeMachine Learning models, parametric models, or any other technicallyfeasible type of model that reflects time-varying behavior. Event models412 are discussed in greater detail below in conjunction with FIG. 5 .

Once event analyzer 410 successfully maps distribution event 402 to anevent model 412, event analyzer 410 generates device identifier (ID) 414to identify the particular type of DER device 210 responsible forcausing distribution event 402. Event analyzer 410 then transmitsdistribution event 402 and device ID 414 to commitment validator 420. Inone embodiment, once event analyzer 410 maps the distribution event 402to the event model 412, event analyzer 410 may then update the eventmodel 412 to reflect one or more characteristics of distribution event402. In doing so, event analyzer 410 may implement Machine Learning tomodify one or more parameters associated with the event model 412. Thisapproach allows event models 412 to remain up-to-date with changingoperational characteristics of DER devices 210.

Commitment validator 420 analyzes commitment 212 to determine theparticular DER device 210 responsible for generating commitment 212.Commitment validator 420 also determines the specific operations the DERdevice 210 performs to satisfy the terms of an energy market contractduring the time interval when those specific operations are to haveoccurred. Commitment validator 420 then processes distribution event 402to confirm that those specific operations did, indeed, occur during thespecified time interval.

For example, suppose commitment 212 indicates that DER device 210(0)begins storing power in a home battery system at a first point in time.Commitment validator 420 could confirm the occurrence of this particularoperation upon determining that event 402 indicates a characteristicincrease in electrical loading at the first point in time. Commitmentvalidator 420 also processes device ID 414 to verify the identity of theDER device 210 responsible for generating commitment 212. In particular,commitment validator 420 determines that commitment 212 and device ID414 are both associated with the same type of DER device 210 or the sameinstance of a DER device 210.

When commitment validator 420 successfully confirms that distributionevent 402 and device ID 414 collectively indicate that the DER device210 performs the operations set forth in commitment 212 during thespecified time interval, commitment validator 420 then generatesvalidated commitment 224. Via the above techniques, validatedcommitments 224 can be generated automatically and in real time.Validated commitments 224 can then be used to expedite a settlementprocess associated with an energy market.

FIG. 5 is a graph of exemplary metrology data that is processed by thenode of FIG. 2 when validating a commitment, according to variousembodiments. As shown, a graph 500 includes metrology data 502 plottedagainst a time axis 510 and a wattage axis 520. Metrology data 502indicates a time-varying wattage associated with a location where node220 is deployed. That wattage decreases at time T₀, increases at T₁, andthen decreases again at time T₂.

In the example described herein, validation application 332 within node220 could analyze metrology data 502 and parameterize the variousfluctuations shown in order to identify these fluctuations with specificDER devices 210. For example, validation application 332 could identifythe slight decrease in wattage at time T₀ with DER device 210(0)powering off based on an event model 412 that characterizes theoperation of DER device 210(0). In this manner, validation application332 can determine signature patterns within metrology data 502 thatuniquely identify specific DER devices 210 and/or specific operationsperformed by DER devices 210. The techniques described herein can alsobe implemented relative to any technically feasible metric other thanwattage, including other electrical parameters and other parametersassociated with resource distribution.

Referring generally to FIGS. 1-5 , any of the techniques discussedherein can be performed by one or more separate elements of networksystem 1. In one embodiment, validation application 332 may be adistributed software application that executes partially on node 220 andpartially in one or more other computing environments. For example, afirst portion of validation application 332 could execute on node 220 togenerate metrology data 222 and distribution events 402. A secondportion of validation application 332 could execute remotely in acloud-based environment to generate validated commitments 224 based ondistribution events 402 and commitments 212. The second portion ofvalidation application 332 could also execute locally on a device thatresides proximate to node 220, such as a local server or a home smartdevice. In either case, when the first portion of validation application332 detects an event, the second portion of validation application 332may retroactively analyze metrology data 222 in greater detail tovalidate one or more commitments 212.

In another embodiment, node 220 may execute a distributed ledger clientin order to share validated commitments 224 with other nodes that alsoexecute the distributed ledger client. For example, node 220 couldexecute a blockchain client to participate in a blockchain network. Inso doing, node 220 could transmit validated commitments 224 to othernodes in a hierarchy of nodes that participate in the blockchainnetwork.

Persons skilled in the art will recognize that the techniques describedherein can be implemented to validate any technically feasible type ofoperation that is performed by any technically feasible type of devicecoupled to any technically feasible type of resource distributioninfrastructure. For example, the disclosed techniques could beimplemented to validate modifications made to the distribution of waterby a device that is coupled (directly or indirectly) to a water main.

Automatically Validating DER Device Commitments

FIG. 6 is a flow diagram of method steps for validating a DER devicecommitment in real time, according to various embodiments. Although themethod steps are described in conjunction with the systems of FIGS. 1-5, persons skilled in the art will understand that any system configuredto perform the method steps in any order falls within the scope of thepresent invention.

As shown, a method 600 begins at step 602, where node 220 of FIG. 1obtains a commitment from a DER device. Node 220 is coupled to a utilityline at a given location and configured to monitor the distribution ofresources to the location via the utility line, in like fashion as shownin FIG. 2 . The DER device is coupled to node 220 in a BTM configurationand can modify the distribution of resources at the location. Forexample, the DER device could be configured to draw resources from theutility line or distribute resources onto the utility line. Theresources could be, for example, electricity, gas, water, or any othertechnically feasible type of resource.

At step 604, node 220 generates metrology data that reflects thedistribution of resources via utility line 230. For example, themetrology data could indicate a kilowatt-hour measurement of electricitydistributed to a residence. The metrology data may include one or morechannels of real-time data having any technically feasible resolution,including one-second resolution (or less).

At step 606, node 220 determines a modification to the distribution ofresources that occurs during a first time interval based on thecommitment. The DER device generates the commitment upon performingvarious operations that modify the distribution of resources at thelocation. The DER device generally performs these operations inaccordance with a contract associated with an energy market. Forexample, the DER device could be a solar power system that distributesat least a minimum amount of power onto the power grid.

At step 608, node 220 identifies a distribution event that occurs duringthe time interval based on the metrology data. As referred to herein, adistribution event refers to a measurable fluctuation in thedistribution of resources at the location, such as, for example, a spikein resource consumption or production, among others. Node 220 can beconfigured to detect any technically feasible type of distribution eventassociated with any type of utility.

At step 610, node 220 confirms that the DER device modifies thedistribution of resources during the time interval based on the event.In particular, node 220 maps the event to the DER device based on alibrary of event models that characterize different DER devices. In sodoing, node 220 validates the authenticity of the DER device and cangenerate a device ID, such as device ID 414 of FIG. 4 . Node 220 alsomaps the event to the modification of resource distribution indicated inthe commitment, thereby confirming that the DER device did, in fact,modify the distribution of resources as reported in the commitment.

At step 612, node 220 generates a validated commitment confirming thatthe DER device modifies the distribution of resources during the timeinterval. Node 220 may then transmit the validated commitment to controlcenter 130 in order expedite a settlement process associated with theenergy market contract. The disclosed approach advantageously generatesvalidated commitments automatically and with minimal oversight.

In sum, a distributed energy resource (DER) device is coupled to autility meter in a “behind-the-meter” configuration. The utility meteranalyzes a commitment generated by the DER device to determine aspecific operation performed by the DER device at a particular time. Theutility meter analyzes metrology data to identify an “event” associatedwith the particular time and then attempts to map the identified eventback to the DER device based on a library of events associated withdifferent DER devices. The utility meter also attempts to map theidentified event to the specific operation set forth in the commitment.If the utility meter can successfully map the identified event to boththe DER device and to the specific operation set forth in thecommitment, then the utility meter generates a validated commitment. Thevalidated commitment can be used to facilitate an energy marketsettlement process.

One technological advantage of the disclosed approach relative to theprior art is that commitments reported by DER devices are validatedautomatically by the utility meter, which enables the settlement processto be performed much faster relative to conventional approaches.Accordingly, consumers can be compensated for participating in energymarkets much faster than the three-month or longer periods typicallyassociated with conventional settlement processes. This technologicaladvantage represents at least one technological advancement relative toprior art approaches.

1. Some embodiments include a computer-implemented method for validatingdistributed resource device commitments, the method comprisingdetermining that a first commitment received from a first distributedresource device indicates that the first distributed resource devicemodified how much of a resource was consumed at a first location duringa first time interval, generating first metrology data that indicateshow much of the resource was consumed at the first location during thefirst time interval, and validating the first commitment based on thefirst metrology data.

2. The computer-implemented method of clause 1, wherein validating thefirst commitment comprises generating a first validated commitmentconfirming that the first commitment indicates that the firstdistributed resource device modified how much of the resource wasconsumed at the first location during the first time interval.

3. The computer-implemented method of any of clauses 1-2, furthercomprising generating a first distributed ledger transaction based onthe first validated commitment, and transmitting the first distributedledger transaction to one or more nodes included in a distributed ledgernetwork.

4. The computer-implemented method of any of clauses 1-3, whereinvalidating the first commitment comprises identifying a first event thatoccurs at the first location based on the first metrology data, anddetermining that the first event indicates that the first distributedresource device modified how much of the resource was consumed at thefirst location during the first time interval.

5. The computer-implemented method of any of clauses 1-4, whereindetermining that the first event indicates that the first distributedresource device modified how much of the resource was consumed at thefirst location during the first time interval comprises mapping thefirst event to a first event model included in a library of eventmodels, wherein the first event model indicates at least one operationalattribute of the first distributed resource device.

6. The computer-implemented method of any of clauses 1-5, furthercomprising modifying the first event model based on the first event,wherein the first event model comprises a Machine Learning model.

7. The computer-implemented method of any of clauses 1-6, wherein thefirst distributed resource device restricts resource consumption at thefirst location to modify how much of the resource was consumed at thefirst location.

8. The computer-implemented method of any of clauses 1-7, wherein thefirst distributed resource device offsets resource consumption at thefirst location via generation of additional resources to modify how muchof the resource was consumed at the first location.

9. The computer-implemented method of any of clauses 1-8, whereingenerating the first metrology data comprises measuring, in real time,one or more electrical characteristics corresponding to an electricalload associated with the first location.

10. The computer-implemented method of any of clauses 1-9, whereinidentifying the first event comprises determining that at least one ofthe one or more electrical characteristics exceeds a threshold value.

11. Some embodiments include a non-transitory computer-readable mediumstoring program instructions that, when executed by a processor, causesthe processor to validate distributed energy resource (DER) devicecommitments by performing the steps of determining that a firstcommitment received from a first distributed resource device indicatesthat the first distributed resource device modified how much of aresource was consumed at a first location during a first time interval,generating first metrology data that indicates how much of the resourcewas consumed at the first location during the first time interval, andvalidating the first commitment based on the first metrology data.

12. The non-transitory computer-readable medium of clause 11, whereinthe step of validating the first commitment comprises generating a firstvalidated commitment confirming that the first commitment indicates thatthe first distributed resource device modified how much of the resourcewas consumed at the first location during the first time interval.

13. The non-transitory computer-readable medium of any of clauses 11-12,wherein the step of validating the first commitment comprisesidentifying a first event that occurs at the first location based on thefirst metrology data, and determining that the first event indicatesthat the first distributed resource device modified how much of theresource was consumed at the first location during the first timeinterval.

14. The non-transitory computer-readable medium of any of clauses 11-13,wherein determining that the first event indicates that the firstdistributed resource device modified how much of the resource wasconsumed at the first location during the first time interval comprisesmapping the first event to a first event model included in a library ofevent models, wherein the first event model indicates at least oneoperational attribute of the first distributed resource device.

15. The computer-implemented method of any of clauses 11-14, wherein thefirst distributed resource device restricts resource consumption at thefirst location or generates additional resources at the location tomodify how much of the resource was consumed at the first location.

16. The computer-implemented method of any of clauses 11-15, wherein thefirst distributed resource device comprises a smart electric device, asmart water device, or a smart gas device.

17. Some embodiments include a system, comprising a memory that stores asoftware application, and a processor that, when executing the softwareapplication, is configured to perform the steps of determining that afirst commitment received from a first distributed resource deviceindicates that the first distributed resource device modified how muchof a resource was consumed at a first location during a first timeinterval, and validating the first commitment based on first metrologydata that indicates how much of the resource was consumed at the firstlocation during the first time interval.

18. The system of clause 17, wherein the processor resides within asmart utility meter that monitors resource consumption at the firstlocation to generate the metrology data.

19. The system of any of clauses 17-18, wherein the processor is furtherconfigured to perform the additional step of obtaining the firstmetrology data from a node that is coupled to a resource distributioninfrastructure and monitors resource consumption at the first locationto generate the metrology data.

20. The system of any of clauses 17-19, wherein the processor validatesthe first commitment by identifying a first event that occurs at thefirst location based on the first metrology data, determining that thefirst event indicates that the first distributed resource devicemodified how much of the resource was consumed at the first locationduring the first time interval, and generating a validated commitmentconfirming that the first commitment indicates that the firstdistributed resource device modified how much of the resource wasconsumed at the first location during the first time interval.

Any and all combinations of any of the claim elements recited in any ofthe claims and/or any elements described in this application, in anyfashion, fall within the contemplated scope of the present invention andprotection.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” In addition, any hardware and/or software technique, process,function, component, engine, module, or system described in the presentdisclosure may be implemented as a circuit or set of circuits.Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine. The instructions, when executed via the processor ofthe computer or other programmable data processing apparatus, enable theimplementation of the functions/acts specified in the flowchart and/orblock diagram block or blocks. Such processors may be, withoutlimitation, general purpose processors, special-purpose processors,application-specific processors, or field-programmable gate arrays.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method comprising: receiving, by a firstutility meter, a first commitment from a first distributed resourcedevice, wherein the first distributed resource device performs one ormore operations to modify a distribution of a resource at a firstlocation during a first time interval; determining, by the first utilitymeter, that the first commitment specifies that the first distributedresource device is a first type of distributed resource device andindicates the one or more operations to modify the distribution of theresource; measuring, by the first utility meter, a real-timedistribution of the resource to generate first real-time metrology datathat indicates how much of the resource was consumed at the firstlocation during the first time interval; identifying, based on a firstevent associated with a first pattern of resource usage indicated in thefirst real-time metrology data, a first event model included in alibrary of event models that associate different patterns of resourceusage with corresponding types of distributed resource devices;determining that a type of distributed resource device associated withthe first event model corresponds to the first type of distributedresource device specified in the first commitment; generating a mappingbetween the first pattern of resource usage associated with the firstevent and the one or more operations to modify the distribution of theresource indicated in the first commitment; validating the firstcommitment based on the mapping and the determining that the type ofdistributed resource device associated with the first event modelcorresponds to the first type of distributed resource device specifiedin the first commitment; and transmitting the validated first commitmentvia a network to a control center, wherein the control center executesone or more software applications to perform one or more operationsbased on the validated first commitment.
 2. The method of claim 1,wherein validating the first commitment comprises generating thevalidated first commitment confirming that the first distributedresource device modified the distribution of the resource at the firstlocation during the first time interval.
 3. The method of claim 2,further comprising: generating a first distributed ledger transactionbased on the validated first commitment; and transmitting the firstdistributed ledger transaction to one or more nodes included in adistributed ledger network.
 4. The method of claim 1, wherein validatingthe first commitment comprises: identifying a second event that occursat the first location based on the first real-time metrology data; anddetermining that the second event indicates that the first distributedresource device modified the distribution of the resource at the firstlocation during the first time interval.
 5. The method of claim 4,wherein determining that the second event indicates that the firstdistributed resource device modified the distribution of the resource atthe first location during the first time interval comprises mapping thesecond event to a second model included in the library of event models,wherein the second model indicates at least one operational attribute ofthe first distributed resource device.
 6. The method of claim 5, furthercomprising modifying the second model based on the second event.
 7. Themethod of claim 1, wherein the first distributed resource devicerestricts resource consumption at the first location to modify thedistribution of the resource at the first location.
 8. The method ofclaim 1, wherein the first distributed resource device offsets resourceconsumption at the first location via generation of additional resourcesto modify the distribution of the resource at the first location.
 9. Themethod of claim 1, wherein generating the first real-time metrology datacomprises measuring, in real time, one or more electricalcharacteristics corresponding to an electrical load associated with thefirst location.
 10. The method of claim 9, wherein the first event isidentified by determining that at least one of the one or moreelectrical characteristics exceeds a threshold value.
 11. One or morenon-transitory computer-readable media storing program instructionsthat, when executed by one or more processors, causes the one or moreprocessors to perform the steps of: receiving, by a first utility meter,a first commitment from a first distributed resource device, wherein thefirst distributed resource device performs one or more operations tomodify a distribution of a resource at a first location during a firsttime interval; determining, by the first utility meter, that the firstcommitment specifies that the first distributed resource device is afirst type of distributed resource device and indicates the one or moreoperations to modify the distribution of the resource; measuring, by thefirst utility meter, a real-time distribution of the resource togenerate first real-time metrology data that indicates how much of theresource was consumed at the first location during the first timeinterval; identifying, based on a first event associated with a firstpattern of resource usage indicated in the first real-time metrologydata, a first event model included in a library of event models thatassociate different patterns of resource usage with corresponding typesof distributed resource devices; determining that a type of distributedresource device associated with the first event model corresponds to thefirst type of distributed resource device specified in the firstcommitment; generating a mapping between the first pattern of resourceusage associated with the first event and the one or more operations tomodify the distribution of the resource indicated in the firstcommitment; validating the first commitment based on the mapping and thedetermining that the type of distributed resource device associated withthe first event model corresponds to the first type of distributedresource device specified in the first commitment; and transmitting thevalidated first commitment via a network to a control center, whereinthe control center executes one or more software applications to performone or more operations based on the validated first commitment.
 12. Theone or more non-transitory computer-readable media of claim 11, whereinthe step of validating the first commitment comprises generating thevalidated first commitment confirming that the first distributedresource device modified the distribution the resource at the firstlocation during the first time interval.
 13. The one or morenon-transitory computer-readable media of claim 11, wherein the step ofvalidating the first commitment comprises: identifying a second eventthat occurs at the first location based on the first real-time metrologydata; and determining that the second event indicates that the firstdistributed resource device modified the distribution of the resource atthe first location during the first time interval.
 14. The one or morenon-transitory computer-readable media of claim 13, wherein determiningthat the second event indicates that the first distributed resourcedevice modified the distribution of the resource at the first locationduring the first time interval comprises mapping the second event to asecond model included in the library of event models, wherein the secondmodel indicates at least one operational attribute of the firstdistributed resource device.
 15. The method of claim 1, wherein thefirst distributed resource device restricts resource consumption at thefirst location or generates additional resources at the first locationto modify the distribution of the resource at the first location. 16.The method of claim 1, wherein the first distributed resource devicecomprises a smart electric device, a smart water device, or a smart gasdevice.
 17. A system, comprising: one or more processors; and one ormore memories storing software that when executed by the one or moreprocessors causes the system to perform operations comprising:receiving, by a first utility meter, a first commitment from a firstdistributed resource device, wherein the first distributed resourcedevice performs one or more operations to modify a distribution of aresource at a first location during a first time interval, determining,by the first utility meter, that the first commitment specifies that thefirst distributed resource device is a first type of distributedresource device and indicates the one or more operations to modify thedistribution of the resource, measuring, by the first utility meter, areal-time distribution of the resource to generate first real-timemetrology data that indicates how much of the resource was consumed atthe first location during the first time interval, identifying, based ona first event associated with a first pattern of resource usageindicated in the first real-time metrology data, a first event modelincluded in a library of event models that associate different patternsof resource usage with corresponding types of distributed resourcedevices, determining that a type of distributed resource deviceassociated with the first event model corresponds to the first type ofdistributed resource device specified in the first commitment,generating a mapping between the first pattern of resource usageassociated with the first event and the one or more operations to modifythe distribution of the resource indicated in the first commitment,validating the first commitment based on the mapping and the determiningthat the type of distributed resource device associated with the firstevent model corresponds to the first type of distributed resource devicespecified in the first commitment, and transmitting the validated firstcommitment via a network to a control center, wherein the control centerexecutes one or more software applications to perform one or moreoperations based on the validated first commitment.
 18. The system ofclaim 17, wherein the one or more processors reside within a smartutility meter that monitors resource consumption at the first locationto generate the first real-time metrology data.
 19. The system of claim17, wherein the operations further comprise obtaining the firstreal-time metrology data from a node that is coupled to a resourcedistribution infrastructure and monitors resource consumption at thefirst location to generate the first real-time metrology data.
 20. Thesystem of claim 17, wherein validating the first commitment comprises:identifying a second event that occurs at the first location based onthe first real-time metrology data; determining that the second eventindicates that the first distributed resource device modified thedistribution of the resource at the first location during the first timeinterval; and generating the validated first commitment confirming thatthe first distributed resource device modified the distribution of theresource at the first location during the first time interval.